
"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import YolosImageProcessor



class 			lowercase					( unittest.TestCase		):






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=7      ,SCREAMING_SNAKE_CASE_  :	List[str]=3      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=30      ,SCREAMING_SNAKE_CASE_  :	Dict=400      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	int=[0.5, 0.5, 0.5]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=[0.5, 0.5, 0.5]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=1 / 255      ,SCREAMING_SNAKE_CASE_  :	Tuple=True      ,)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
				lowerCAmelCase												=  parent
				lowerCAmelCase												=  batch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  min_resolution
				lowerCAmelCase												=  max_resolution
				lowerCAmelCase												=  do_resize
				lowerCAmelCase												=  size
				lowerCAmelCase												=  do_normalize
				lowerCAmelCase												=  image_mean
				lowerCAmelCase												=  image_std
				lowerCAmelCase												=  do_rescale
				lowerCAmelCase												=  rescale_factor
				lowerCAmelCase												=  do_pad






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Tuple:





				"""simple docstring"""


				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]=False			)       ->	List[Any]:





				"""simple docstring"""


				if not batched:
					lowerCAmelCase												=  image_inputs[0]
					if isinstance(SCREAMING_SNAKE_CASE_      ,Image.Image			):
						lowerCAmelCase							,       lowerCAmelCase												=  image.size
					else:
						lowerCAmelCase							,       lowerCAmelCase												=  image.shape[1], image.shape[2]
					if w < h:
						lowerCAmelCase												=  int(self.size['''shortest_edge'''] * h / w			)
						lowerCAmelCase												=  self.size['''shortest_edge''']
					elif w > h:
						lowerCAmelCase												=  self.size['''shortest_edge''']
						lowerCAmelCase												=  int(self.size['''shortest_edge'''] * w / h			)
					else:
						lowerCAmelCase												=  self.size['''shortest_edge''']
						lowerCAmelCase												=  self.size['''shortest_edge''']

				else:
					lowerCAmelCase												=  []
					for image in image_inputs:
						lowerCAmelCase							,       lowerCAmelCase												=  self.get_expected_values([image]			)
						expected_values.append((expected_height, expected_width)			)
					lowerCAmelCase												=  max(SCREAMING_SNAKE_CASE_      ,key=lambda SCREAMING_SNAKE_CASE_			: item[0]			)[0]
					lowerCAmelCase												=  max(SCREAMING_SNAKE_CASE_      ,key=lambda SCREAMING_SNAKE_CASE_			: item[1]			)[1]

				return expected_height, expected_width





@require_torch
@require_vision
class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     YolosImageProcessor if is_vision_available() else None






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  YolosImageProcessingTester(self			)






			@property
			def 					UpperCAmelCase							(self  :	int			)       ->	Dict:





				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_mean'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_std'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_normalize'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_resize'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''size'''			)			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class.from_dict(self.image_processor_dict			)
				self.assertEqual(image_processor.size      ,{'''shortest_edge''': 18, '''longest_edge''': 1_333}			)
				self.assertEqual(image_processor.do_pad      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self.image_processing_class.from_dict(
				    self.image_processor_dict      ,size=42      ,max_size=84      ,pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_			)
				self.assertEqual(image_processor.size      ,{'''shortest_edge''': 42, '''longest_edge''': 84}			)
				self.assertEqual(image_processor.do_pad      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				pass






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random PIL images
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,Image.Image			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values

				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

				self.assertEqual(
				    encoded_images.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

				# Test batched
				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      ,batched=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,)






			def 					UpperCAmelCase							(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random numpy tensors
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,numpify=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,np.ndarray			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values

				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

				self.assertEqual(
				    encoded_images.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

				# Test batched
				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values

				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      ,batched=SCREAMING_SNAKE_CASE_			)

				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random PyTorch tensors
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,torchify=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,torch.Tensor			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values

				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

				self.assertEqual(
				    encoded_images.shape      ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,)

				# Test batched
				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values

				lowerCAmelCase							,       lowerCAmelCase												=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      ,batched=SCREAMING_SNAKE_CASE_			)

				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )      ,)






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Any:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				lowerCAmelCase												=  self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE_      ,do_normalize=SCREAMING_SNAKE_CASE_      ,do_rescale=SCREAMING_SNAKE_CASE_			)
				# create random PyTorch tensors
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,torchify=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,torch.Tensor			)

				# Test whether the method "pad" and calling the image processor return the same tensors
				lowerCAmelCase												=  image_processing_a.pad(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			)
				lowerCAmelCase												=  image_processing_a(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			)

				self.assertTrue(
				    torch.allclose(encoded_images_with_method['''pixel_values''']      ,encoded_images['''pixel_values''']      ,atol=1e-4			)			)






			@slow
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
				with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''      ,'''r'''			) as f:
					lowerCAmelCase												=  json.loads(f.read()			)

				lowerCAmelCase												=  {'''image_id''': 39_769, '''annotations''': target}

				# encode them
				lowerCAmelCase												=  YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''			)
				lowerCAmelCase												=  image_processing(images=SCREAMING_SNAKE_CASE_      ,annotations=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			)

				# verify pixel values
				lowerCAmelCase												=  torch.Size([1, 3, 800, 1_066]			)
				self.assertEqual(encoding['''pixel_values'''].shape      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-4			)			)

				# verify area
				lowerCAmelCase												=  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify boxes
				lowerCAmelCase												=  torch.Size([6, 4]			)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)
				# verify image_id
				lowerCAmelCase												=  torch.tensor([39_769]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify is_crowd
				lowerCAmelCase												=  torch.tensor([0, 0, 0, 0, 0, 0]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify class_labels
				lowerCAmelCase												=  torch.tensor([75, 75, 63, 65, 17, 17]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify orig_size
				lowerCAmelCase												=  torch.tensor([480, 640]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify size
				lowerCAmelCase												=  torch.tensor([800, 1_066]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,SCREAMING_SNAKE_CASE_			)			)






			@slow
			def 					UpperCAmelCase							(self  :	Dict			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
				with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''      ,'''r'''			) as f:
					lowerCAmelCase												=  json.loads(f.read()			)

				lowerCAmelCase												=  {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

				lowerCAmelCase												=  pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

				# encode them
				lowerCAmelCase												=  YolosImageProcessor(format='''coco_panoptic'''			)
				lowerCAmelCase												=  image_processing(images=SCREAMING_SNAKE_CASE_      ,annotations=SCREAMING_SNAKE_CASE_      ,masks_path=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			)

				# verify pixel values
				lowerCAmelCase												=  torch.Size([1, 3, 800, 1_066]			)
				self.assertEqual(encoding['''pixel_values'''].shape      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-4			)			)

				# verify area
				lowerCAmelCase												=  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify boxes
				lowerCAmelCase												=  torch.Size([6, 4]			)
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]      ,SCREAMING_SNAKE_CASE_      ,atol=1e-3			)			)
				# verify image_id
				lowerCAmelCase												=  torch.tensor([39_769]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify is_crowd
				lowerCAmelCase												=  torch.tensor([0, 0, 0, 0, 0, 0]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify class_labels
				lowerCAmelCase												=  torch.tensor([17, 17, 63, 75, 75, 93]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify masks
				lowerCAmelCase												=  822_873
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()      ,SCREAMING_SNAKE_CASE_			)
				# verify orig_size
				lowerCAmelCase												=  torch.tensor([480, 640]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']      ,SCREAMING_SNAKE_CASE_			)			)
				# verify size
				lowerCAmelCase												=  torch.tensor([800, 1_066]			)
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']      ,SCREAMING_SNAKE_CASE_			)			)




"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


